6 papers
On Automated and Explainable Provenance of AI-Generated Code
Alejandro Velasco, Nathan Wintersgill, Trevor Stalnaker +2
Generative AI for code generation has transformed software development, but it has also introduced a critical transparency problem: the origins of AI-generated code are opaque to t…
"Don't Be Afraid, Just Learn": Insights from Industry Practitioners to Prepare Software Engineers in the Age of Generative AI
Daniel Otten, Trevor Stalnaker, Nathan Wintersgill +3
Although tension between university curricula and industry expectations has existed in some form for decades, the rapid integration of generative AI (GenAI) tools into software dev…
Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools
Daniel Otten, Trevor Stalnaker, Nathan Wintersgill +2
The use of generative AI (GenAI) tools has fundamentally transformed software development. Central to this shift is prompt engineering, the practice of crafting textual prompts to…
Developers' Perspectives on Software Licensing: Current Practices, Challenges, and Tools
Nathan Wintersgill, Trevor Stalnaker, Daniel Otten +5
Most modern software products incorporate open-source components, requiring development teams to maintain compliance with each component's licenses. Noncompliance can have signific…
An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face
Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro +4
The last decade has seen widespread adoption of Machine Learning (ML) components in software systems. This has occurred in nearly every domain, from natural language processing to…
Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Development
Trevor Stalnaker, Nathan Wintersgill, Oscar Chaparro +4
Despite the utility that Generative AI (GenAI) tools provide for tasks such as writing code, the use of these tools raises important legal questions and potential risks, particular…